Google's T5 for Closed Book Question Answering.
Note: This model should be fine-tuned on a question answering downstream task before it is useable for closed book question answering.
Other Community Checkpoints: here
Authors: Adam Roberts, Colin Raffel, Noam Shazeer
It has recently been observed that neural language models trained on unstructured text can implicitly store and retrieve knowledge using natural language queries. In this short paper, we measure the practical utility of this approach by fine-tuning pre-trained models to answer questions without access to any external context or knowledge. We show that this approach scales with model size and performs competitively with open-domain systems that explicitly retrieve answers from an external knowledge source when answering questions. To facilitate reproducibility and future work, we release our code and trained models at https://goo.gle/t5-cbqa.
- Downloads last month